[Paper Review] Cognitive UAV Communication via Joint Trajectory and Power Control

Yuwei Huang, Jie Xu+2 more|arXiv (Cornell University)|Feb 14, 2018

UAV Applications and Optimization12 references3 citations

TL;DR

This paper proposes a joint trajectory and power control scheme for cognitive UAV communications to maximize the average achievable rate while protecting primary terrestrial users via interference temperature constraints. By using alternating optimization and successive convex approximation, the method achieves significant rate gains over benchmarks by intelligently balancing UAV mobility and transmit power to minimize interference and exploit favorable channel conditions.

ABSTRACT

This paper investigates a new spectrum sharing scenario between unmanned aerial vehicle (UAV) and terrestrial wireless communication systems. We consider that a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV's controllable mobility via trajectory design, to improve the cognitive UAV communication performance while controlling the co-channel interference at each of the primary receivers (PRs). In particular, we maximize the average achievable rate from the UAV to the SR over a finite mission/communication period by jointly optimizing the UAV trajectory and transmit power allocation, subject to constraints on the UAV's maximum speed, initial/final locations, and average transmit power, as well as a set of interference temperature (IT) constraints imposed at each of the PRs for protecting their communications. However, the joint trajectory and power optimization problem is non-convex and thus difficult to be solved optimally. To tackle this problem, we propose an efficient algorithm that ensures to obtain a locally optimal solution by applying the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed joint UAV trajectory and power control scheme significantly enhances the achievable rate of the cognitive UAV communication system, as compared to benchmark schemes.

Motivation & Objective

• To address the challenge of co-channel interference in UAV-terrestrial spectrum sharing scenarios.
• To maximize the average achievable rate of a cognitive UAV-to-ground link over a finite mission period.
• To jointly optimize UAV trajectory and transmit power under mobility and interference constraints.
• To ensure primary users' communications are protected via interference temperature (IT) constraints.
• To develop an efficient algorithm for solving the non-convex optimization problem.

Proposed method

• Formulates a non-convex optimization problem to maximize the average achievable rate of the UAV-SR link.
• Applies interference temperature (IT) constraints to limit co-channel interference at each primary receiver (PR).
• Uses alternating optimization to iteratively optimize trajectory and power allocation.
• Employs successive convex approximation (SCA) to transform the non-convex subproblems into convex approximations.
• Imposes constraints on UAV maximum speed, initial/final locations, average transmit power, and IT thresholds at PRs.
• Solves the problem via an iterative algorithm that converges to a locally optimal solution.

Experimental results

Research questions

• RQ1 How can UAV mobility and transmit power be jointly optimized to maximize cognitive UAV communication rate?
• RQ2 What is the impact of trajectory design versus power control alone on system performance?
• RQ3 How does the UAV adapt its flight path to minimize interference to primary terrestrial links?
• RQ4 What performance gain is achievable through joint trajectory and power optimization compared to separate optimization?
• RQ5 How does communication duration affect the performance trade-off between throughput and interference control?

Key findings

• The proposed joint trajectory and power control scheme outperforms all benchmark schemes in terms of average achievable rate.
• When communication duration T ≤ 60 s, power optimization alone yields better performance than trajectory optimization, indicating power control dominates in short missions.
• For T ≥ 70 s, trajectory optimization becomes more effective, showing that mobility gains are fully exploited over longer durations.
• The UAV trajectory deviates from a straight line when interference constraints are tight (e.g., Γ = -90 dBm), moving away from primary receivers to reduce interference.
• The UAV slows down near the secondary receiver (SR) to exploit favorable channel conditions, while flying faster near primary receivers to minimize interference exposure.
• Numerical results confirm that joint optimization achieves significant spectral efficiency gains by balancing throughput and co-channel interference control.